System and method to automatically discriminate between different data types

ABSTRACT

There is described a system and method for automatically discriminating between different types of data with an image reader. In brief overview of one embodiment, the automatic discrimination feature of the present image reader allows a human operator to aim a hand held image reader at a target that can contain a dataform and actuate the image reader. An autodiscrimination module in the image reader in one embodiment analyzes image data representative of the target and determines a type of data represented in the image data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.13/083,825 filed Apr. 11, 2011 entitled “System and Method toAutomatically Discriminate Between Different Data Types” which is adivisional of U.S. patent application Ser. No. 12/884,946 filed Sep. 17,2010 entitled “System and Method to Automatically Discriminate BetweenDifferent Data Types” (now U.S. Pat. No. 7,922,088) which is adivisional of U.S. patent application Ser. No. 11/903,747 filed Sep. 24,2007 entitled “System and Method to Automatically Discriminate Between aSignature and a Bar Code” (now abandoned) which is a continuation ofU.S. patent application Ser. No. 10/958,779 filed Oct. 5, 2004 entitled,“System And Method To Automatically Discriminate Between A Signature AndA Dataform” (now U.S. Pat. No. 7,293,712). The above applications (U.S.patent application Ser. Nos. 13/083,825, 12/884,946, 11/903,747, and10/958,779) are incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The invention relates to data collection in general and particularly toa system and method for automatically discriminating between differentdata types.

BACKGROUND OF THE INVENTION

Some currently available data collection devices, such as hand-heldimage readers, are capable of capturing signatures as well as readingbarcodes. The reading and decoding of a barcode represents an operationdistinct from that involved in capturing a signature. The reading anddecoding of a bar code involves the imaging and then decoding of a oneor two dimensional graphic symbol into the alphanumeric sequence encodedby the symbol. The capturing of a signature involves identifying aregion of interest and then storing an electronic visualcopy/representation of the identified region of interest.

One approach to enabling the operation of an image reader as a barcodedecoder and a signature capture device involves the input of a humanoperator. According to this approach, the human operator manuallyswitches the mode of operation of the image reader between barcodedecoding and signature capturing. One disadvantage of this approach isthat the manual selection process requires additional training forpersonnel. This additional training is in contradiction with the idealthat the operational details of an image reader should be abstracted asmuch as possible. For example, the human operator should be able toemploy the image reader by simply pointing the device and pulling anactivation trigger. Another disadvantage is that the manual selectionprocess can be time consuming. In the field of data collection, apremium is placed on efficient quick operation and even slight delayscan be significant. The significance of these delays is compounded bythe large number of times the barcode read or signature captureoperation is activated in an image reader during a typical session.

Another approach to enabling the operation of an image reader as abarcode decoder and a signature capture device involves using a dataformdecode operation to identify and locate the signature capture area. Thedataform approach suffers from several drawbacks. One drawback is thatif the dataform, such as a linear barcode or two dimensional symbology,has been damaged due to handling or defected through printing, thedataform decode failure will render the signature capture inoperable.Another drawback of the dataform approach is that space constraints canmake it difficult to accommodate the space required for a dataform. Forexample on some shipping labels and on some driver's licenses, littlefree space is available for additional graphic elements. A furtherdrawback of the dataform approach is that its operation is unintuitive.Although the human operator desires to capture the signature, he or shemust aim the reader at the dataform instead of the signature. The resultof this unintuitive approach is that frequently the signature image willbe clipped due to improper orientation of the image reader. To achievemore acceptable operation, additional human operator training andfeedback, with its commensurate costs and burdens, is often required.

Hence, there is a need for an image reader that facilitates thecapturing of signatures while maintaining the image reader's ease ofoperation as a bar code reader.

SUMMARY OF THE INVENTION

There is described a system and method for automatically discriminatingbetween different types of data with an image reader. In brief overviewof one embodiment, the automatic discrimination feature of the presentimage reader allows a human operator to aim a hand held image reader ata target that can contain a dataform and actuate the image reader. Anautodiscrimination module in the image reader in one embodiment analyzesimage data representative of the target and determines a type of datarepresented in the image data.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and features of the invention can be better understood withreference to the drawings described below, and the claims. The drawingsare not necessarily to scale, emphasis instead generally being placedupon illustrating the principles of the invention. In the drawings, likenumerals are used to indicate like parts throughout the various views.

FIG. 1 shows an embodiment of an image reader constructed in accordancewith the principles of the invention employed in a package shippingenvironment.

FIG. 2 shows a shipping label from the package shipping environment ofFIG. 1 with a plurality of data types used in accordance with theinvention.

FIG. 3 is schematic block diagram of an image reader constructed inaccordance with the principles of the invention.

FIG. 4 is a schematic block diagram showing details of the image readerof FIG. 3.

FIG. 5 shows a process for practicing the principles of the inventionincluding automatically discriminating between different data types.

FIG. 6A shows one embodiment of a plurality of curvelet detector mapsconstructed in accordance with the principles of the invention.

FIG. 6B shows another embodiment of a plurality of curvelet detectormaps constructed in accordance with the principles of the invention.

FIG. 7 is a diagrammatic representation of a histogram analysisperformed in one embodiment of the invention.

FIGS. 8A-8D are diagrammatic representations of an image datasegmentation processes according to embodiments of the invention.

FIG. 9 shows a process for practicing the principles of the inventionincluding reading and decoding a dataform and capturing a signature.

FIG. 10 is perspective drawing of a hand-held image reader constructedaccording to the principles of the invention.

FIGS. 11A-11D show perspective drawings of a portable data terminalembodiment of an image reader constructed in accordance with theprinciples of the invention.

FIG. 12 shows an electrical block diagram of one embodiment of theportable data terminal of FIGS. 11A-11D.

FIG. 13A is a progressive random pattern constructed in accordance withone embodiment of the invention for use in capturing a signature.

FIG. 13B is a progressive random pattern with a signature constructed inaccordance with one embodiment of the invention.

FIG. 13C is a progressive random pattern with a signature constructed inaccordance with another embodiment of the invention.

FIG. 14 shows a process for capturing a signature using a progressiverandom pattern in accordance with one embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention features a system and method for automaticallydiscriminating between different types of data, such as a barcode and asignature, with an image reader. In brief overview of one embodiment,the automatic discrimination feature of the present image reader allowsa human operator to aim a hand-held image reader at a target containinga dataform, a signature, or another element and actuate the trigger onthe image reader. An autodiscrimination module in the image readeranalyzes image data representative of the target and determines whetherthe target contains a dataform, a signature, or another type of data. Ifthe target contains a dataform, the image reader decodes the dataformand outputs decoded dataform data, such as the alphanumeric sequenceencoded by a barcode. If the target contains a signature, the imagereader captures a representation of the signature. For other types ofdata, the image reader performs a correspondingly appropriate operationsuch as optical character recognition (OCR) decoding for printed text.In a further embodiment, the image reader is able to automaticallyidentify a plurality of data types. The data types that can beautomatically discriminated or identified according to the inventioninclude signatures; dataforms; handwritten text; typed text; machinereadable text; OCR data; graphics; pictures; images; forms such asshipping manifests, bills of lading, ID cards, and the like; fingerprints; biometrics such as facial images, retinal scans, and equivalencethereof. As used herein a dataform refers to an originally machinegenerated symbology that is also machine readable such as a 1-D barcode,a 2-D barcode, a 1-D stacked barcode, a logo, glyphs, color-codes, andthe like.

In one embodiment, the autodiscrimination module comprises a dataformdecoder module and an image processing and analysis module. The imageprocessing and analysis module further comprises a feature extractionmodule and a general classifier module. The feature extraction moduleperforms a texture analysis of the image data to generate a sequence ofnumeric outputs representative of the texture of the image data.Examples of the various types of texture that the image data can haveare barcode texture, signature texture, hand-written text texture,printed text texture, graphics texture, picture texture, image texture,and the like. In one embodiment, the texture analysis includes testingthe image data for the presence of characteristics indicative of asignature, such as the presence of curved elements. The numeric outputsof the feature extraction module are used as inputs to the generalclassifier module to classify the image data. In one embodiment, if thedataform decoder module has failed to successfully decode the image dataand the general classifier module indicates that a signature is present,the image reader automatically stores a full or an encodedrepresentation of the signature.

With the autodiscrimination module, an image reader of the presentinvention provides an intuitive straightforward and efficient system tocollect image data that can have varied content including dataforms andsignatures. No special signature capture forms or signature locatingbarcodes are required. When using one embodiment of an image reader ofthe present invention, a human operator intuitively points the readerdirectly at the data to be collected, regardless of its type, andactuates a trigger. A successful dataform decode, signature capture, orthe like is indicated with a common operator feedback mechanism such asa beep tone.

Referring to FIG. 1, a package transport or shipping environment 10 inwhich an image reader 14 of the present invention is employed is shown.Carrier service providers, such as Federal Express™, United PostalService™, the United States Postal Service™, and the like, can employthe environment shown to ship and track items. As part of logging intransport items 16, such as packages 18 and letter envelopes 22, a humanoperator 26, such as a shipping agent, uses the image reader 14 to imagedata from shipping labels 30 on the transport items 16. FIG. 2 showsdetails of a shipping label 30 with various types of data 34 that canautomatically be collected by the image reader 14. The collectionprocess involves the human operator actuating the same trigger 32 on theimage reader 14 for any data type to be read. The data 34 shown includesa dataform 34 a, such as a one-dimensional barcode encoding a labelnumber, a signature 34 b, hand-written text 34 c, and typed text 34 d.In some embodiments, additional elements on the label 30 can be treatedas graphic elements 34 e that contain information. In operation theimage reader 14, identifies the nature of the imaged data and performsthe appropriate action. For the dataform 34 a, the image reader 14decodes the dataform 34 a and creates a shipping record based on theencoded dataform 34 a alphanumeric sequence. For the signature 34 b, theimage reader 14 captures a representation of the signature 34 b. For thehandwritten text 34 c, the image reader can capture an image and canpossibly perform additional analysis. In one embodiment, the additionalanalysis includes locating and decoding the zip code 38 written by thesender. As shown, in some embodiments, locating and decoding the zipcode can be aided by start 38′ and stop patterns 38″ and by boxes 38′″used to enclose the zip code 38 numerals. For the typed text 34 d, theimage reader 14 identifies the data 34 d as typed text and performs OCRdecoding. In one embodiment, the checked boxes 36 in the Shipping andBilling Instructions are treated as graphic elements 34 e that areimaged and decoded to determine the information contained within. Inanother embodiment, the graphic elements 34 e are simply identified asgraphic elements and an image of them is captured. The decoding of labelspecific elements, such as in some embodiments the graphic elements 34 eor the start 38′, stop 38″ and bounding patterns 38′″, can be enhancedby specifically developed software, firmware, and/or dedicated circuitsincluded in the image reader 14.

Once data 34 from the label 30 has been collected by the image reader14, data can be transmitted to a package tracking system 42 via abi-directional communications network 44. The bi-directionalcommunications network 44 can include a wired communications systemincluding wired (e.g., a medium such as electrical wire, Ethernet andoptical fiber) connections through a switching system, such as thetelephone system, or a packet based system, such as the internet. Thebi-directional communications network 44 can also include a wirelesscommunications system, such as one employing electromagnetic signalsusing radio or satellite transmission. In addition the bi-directionalcommunications network 44 could comprise a system including both wiredand wireless links. Communication between the image reader 14 and thebi-directional communications network 44 can occur directly or can befacilitated by, for example, a transmissions cradle 48 that has a directwired or wireless connection with the bi-directional communicationsnetwork 44. As shown, once data 34 from the transport items 16 has beencollected, various transportation means 52 are used to transfer thetransport items 16 as part of their shipment.

In FIG. 3 is shown a schematic block diagram of the image reader 14according to one embodiment of the invention. The image reader 14 caninclude one or more of: a processor module 104, a memory module 108, anI/O module 112, an image collection module 116, an automaticdiscrimination module 120, an actuation module 124, a user feedbackmodule 128, display module 132, a user interface module 134, a radiofrequency identification (RFID) module 136, a smart card module 140,and/or a magnetic stripe card module 144. In a various embodiments eachof the modules is in combination with one or more of the other modules.

FIG. 4 shows one embodiment of the autodiscrimination module 120 thatcomprises a dataform decode module 204 and an image processing andanalysis module 208 that are in communication with each other. As shownin this embodiment, the image processing and analysis module 208comprises a feature extraction module 212, a generalized classifiermodule 216, a signature data processing module 218, an OCR decode module222, and a graphics analysis module 224 that are in communication witheach other. In addition as shown in FIG. 4, the feature extractionmodule 212 comprises a binarizer module 226, a line thinning module 228,and a convolution module 230 that are in communication with each other.

FIG. 5 shows a process 300 for employing one embodiment of the inventionutilizing the autodiscrimination module shown in FIG. 4. The process 300comprises an image reader recording an actuation event (step 302), suchas a trigger pull, and in response collecting (step 304) image data froma target with the image reader 14. After collection, the image data istransferred (step 308) to the dataform decode module 204. The dataformdecode module searches (step 310) the image data for markers, such as aquiet zone, indicative of the presence of a dataform, such as a one ortwo dimensional barcode. If a potential dataform is located, thedataform decode module 204 applies (step 314) one or more dataformdecoding algorithms to the ensuing image data. If the decode attempt issuccessful, the image reader 14 outputs (step 318) decoded dataform dataand signals (step 322) a successful read with an alert, such as a beeptone.

In one embodiment if the decode attempt is not successful, the imagedata is transferred (step 326) to the image processing and analysismodule 208. In another embodiment, the image data is processed inparallel with the attempt to decode the dataform data. In one suchembodiment, the process that completes first (i.e., dataform decodeattempt or the image processing) outputs its data (e.g., a decodedbarcode or a captured signature) and the other parallel process isterminated. In a further embodiment, the image data is processed inresponse to the decoding of the dataform. In one such embodiment, abarcode encodes item information such as shipping label number andinformation indicating that a signature should be captured.

Within the image processing and analysis module 208, the image data isprocessed by the feature extraction module 212. In general, the featureextraction module generates numeric outputs that are indicative of thetexture of the image data. As indicated above, the texture of the imagedata refers to the characteristics of the type of data contained in theimage data. Common types of texture include one or two dimensionalbarcode texture, signature texture, graphics texture, typed texttexture, hand-written text texture, drawing or image texture, photographtexture, and the like. Within any category of textures, sub-categoriesof texture are sometime capable of being identified.

As part of the processing of the image data by the feature extractionmodule 212, the image data is processed (step 328) by the binarizermodule 226. The binarizer module 226 binarizes the grey level image intoa binary image according to the local thresholding and target image sizenormalization. With the image data binarized, the image data isprocessed (step 332) by the line thinning module 228 to reducemulti-pixel thick line segments into single pixel thick lines. Withbinarized line thinned image data, the image data is processed (step336) by the convolution module 230.

In general the convolution module 230 convolves the processed image datawith one or more detector maps designed according to the invention toidentify various textural features in the image data. In one embodiment,the convolution module 230 generates a pair of numbers, the mean andvariance (or standard deviation), for each convolved detector map. FIG.6A shows a set of 12 2×3 binary curvelet detector maps 250 used todetect curved elements present in image data. As each of the curveletdetector maps 250 is convolved with the image data, the mean value andthe variance generated provide an indication of the presence or densityof elements in the binarized line thinned image data having similarshapes to the curvelet detector maps 250. As each pixel map generates apair of numbers, the 12 curvelet detector maps 250 generate a total of24 numbers. According to one embodiment, these 24 numbers arerepresentative of the curved or signature texture of the processed imagedata.

Further processing of the image data includes the outputs from thefeature extraction module 212 being fed (step 340) into the generalizedclassified module 216. The generalized classifier module 216 uses thenumbers generated by the feature extraction module as inputs to a neuralnetwork, a mean square error classifier or the like. These tools areused to classify the image data into general categories. In embodimentsemploying neural networks, different neural network configurations arecontemplated in accordance with the invention to achieve differentoperational optimizations and characteristics. In one embodimentemploying a neural network, the generalized classifier module 212includes a 24+12+6+1=43 nodes Feedforward, Back Propagation Multilayerneural network. The input layer has 24 nodes for the 12 pairs of meanand variance outputs generated by a convolution module 230 employing the12 curvelet detector maps 250. In the neural network of this embodiment,there are two hidden layers of 12 nodes and 6 nodes respectively. Thereis also one output node to report the positive or negative existence ofa signature.

In another embodiment employing a neural network, the 20 curveletdetector maps 260 shown in FIG. 6B are used by the convolution module230. As shown, the 20 curvelet detector maps 260 include the original 12curvelet detector maps 250 of FIG. 6A. The additional 8 pixel maps 260′are used to provide orientation information regarding the signature. Inone embodiment employing the 20 curvelet detector maps 260, thegeneralized classifier module 216 is a 40+40+20+9=109 nodes Feedforward,Back Propagation Multiplayer neural network. The input layer has 40nodes for the 20 pairs of mean and variance outputs generated by aconvolution module 230 employing the 20 curvelet detector maps 260. Inthe neural network of this embodiment, there are two hidden layers of 40nodes and 20 nodes respectively, one output node to report the positiveor negative existence of a signature, and 8 output nodes to report thedegree of orientation of the signature. The eight output nodes provide2⁸=256 possible orientation states. Therefore, the orientation angle isgiven in degrees between 0 and 360 in increments of 1.4 degrees.

In some embodiments, the generalized classifier module 216 is capable ofclassifying data into an expanded collection of categories. For examplein some embodiments, the generalized classifier module 216 specifieswhether the image data contains various data types such as a signature;a dataform; handwritten text; typed text; machine readable text; OCRdata; graphics; pictures; images; forms such as shipping manifest, billof lading, ID cards, and the like; fingerprints, biometrics such asfingerprints, facial images, retinal scans and the like, and/or othertypes of identifiers. In further additional embodiments, the generalizedclassifier module 216 specifies whether the image data includes variouscombinations of these data types. In some embodiments, the generalclassifier module 216 specifies whether the image data contains aspecified type of data or not. In one such embodiment, the imageprocessing and analysis module 208 is contained within an identificationmodule that outputs an affirmative or negative response depending on thepresence or absence of the specified data type, such as a signature or abiometric, in the image data.

In one embodiment once the presence of a signature has been confirmedand its general orientation determined, image data is transferred (step344) to the signature data processing module 218. In one embodiment, thesignature data processing module 218 is used to detect the boundaries ofthe signature in the image data. In one embodiment, the signatureboundary is detected using a histogram analysis. As shown in FIG. 7, ahistogram analysis consists of a series of one-dimensional slices alonghorizontal and vertical directions defined relative to the orientationof the signature. In one embodiment, the value for each one-dimensionalslice corresponds to the number of black (i.e., zero valued) pixelsalong that pixel slice. In some embodiments if no barcodes have beendecoded, then some specified region of the full frame of image data,such as a central region is captured for signature analysis. Oncecompleted, the histogram analysis provides a two-dimensional plot of thedensity of data element pixels in the image data. The boundary of thesignature is determined with respect to a minimum density that must beachieved for a certain number of sequential slices. In one embodiment,the histogram analysis searches inwardly along both horizontal andvertical directions until the pixel density rises above a predefinedcut-off threshold. So that the signature data is not inadvertentlycropped, it is common to use low cut-off threshold values.

In one embodiment, once the boundaries of the signature have beendetermined, the signature data processing module 218 crops the imagedata and extracts the signature image data. In one such embodiment, thecropping is performed by an image modification module that generatesmodified image data in which a portion of the image data not includingthe signature has been deleted. In other embodiments, variouscompression techniques are employed to reduce the memory requirementsfor the signature image data. One such technique includes the encodingof the signature image data by run length encoding. According to thistechnique, the length of each run of similar binarized values (i.e., thelength of each run of 1 or 0) for each scan line is recorded as a meansof reconstructing a bit map. Another encoding technique treats thesignature image data as a data structure where the elements of the datastructure consist of vectors. According this encoding technique, thesignature is broken down into a collection of vectors. The position ofeach vector in combination with the length and orientation of eachvector is used to reconstruct the original signature. In one suchembodiment, the encoding process generates a new vector whenever thecurvature for a continuous pixel run exceeds a specified value. Afurther compression technique employs B-Spline curve fitting. Thistechnique has the capacity to robustly accommodate curvature and scalingissues.

In various embodiments, the signature image data or a compressed orencoded version of the signature image data is stored locally on adedicated memory device. In one such embodiment, the local memory devicecan be detachable memory device such as a CompactFlash memory card orthe like described in more detail below. In another embodiment, thesignature image data is stored in a volatile or non-volatile portion ofgeneral purpose memory and downloaded at a future time. In a furtherembodiment, the signature image data can be transmitted via wired orwireless means either at the time of capture or at a later point, suchas when a data collection session has been completed. One example of thecompletion of a data collection session that results in the transmissionof data is when a shipping agent returns to his or her vehicle withitems that have been recently logged. Once in the vehicle the shippingagent places the image reader 14 in a transmission cradle 48 thattransfers the data to the package tracking system 42 via, for example, aGSM/GPRS communications link.

In another embodiment, the signature data processing module 218 does notperform a histogram analysis but simply stores in memory the entireimage or a compressed version once the presence of a signature has beendetermined. In a further embodiment to save processing time, the initialimage analysis is performed on a lower resolution image. Once thepresence of a signature is determined in this embodiment, a higherresolution image is taken. In one embodiment, a signature extractionhistogram analysis is performed on this image. Next, the image is storedin memory in either compressed or original format. In some embodiments,the image data is combined with other data to form a record for aparticular item such as a package 18 or shipping envelope 22. Asmentioned above, some of the additional data that can be collected bythe image reader 14 and stored with or separate from the signature dataincludes but is not limited to dataform data, handwritten text data,typed text data, graphics data, image or picture data, and the like.

As part of its operations, the image processing and analysis module 208can be designed to perform specialized tasks for different data types.For example, if the generalized classifier module 216 determines thatthe image data contains typed or machine readable text, the image datacan be collected, possibly histogram analyzed, and stored oralternatively the image data can be transferred to the OCR decodingmodule 222. Similarly, if the generalized classifier module 216determines that the image data includes a graphic element, the imagedata can be transferred to the graphics analysis module 224 forprocessing. In one embodiment, the graphics analysis module 224 isconfigured to recognize and decode predefined graphics. In one suchembodiment as discussed above, the graphics analysis can includedetermining which, if any, boxes have been selected in the billing andshipping instructions 34 e on a shipping label 30. In a furtherembodiment, the graphics analysis can include locating and decoding thetyped or handwritten text contained in the zip code box 38 on a shippinglabel 30. In an alternative embodiment, the image reader 14 can beconfigured to automatically attempt decode operations in addition to thedataform decode, such as OCR decoding or graphics decoding, prior to theactivation of the feature extraction module 212.

In another embodiment, the image processing and analysis module 208segments the image data into regions and performs a feature extractionand general classification analysis on each region. In one embodiment asshown in FIG. 8A, the standard rectangular image data window is dividedinto four equal sized sub-rectangles. In another embodiment shown inFIG. 8B, the segmentation consists of overlapping regions so that thetotal area of the segmented regions is larger than that of the completefield of the image data. In FIG. 8B there are seven shown overlappingregions where each identifying numeral is shown in the center of itsregion. In a further embodiment shown in FIGS. 8C and 8D, thesegmentation consists of sample regions (shown as cross-hatched) withinthe complete field of the image data. In another embodiment, the sampledregions can be based on a preloaded user template that, for example,identifies regions of interest such as a signature region and/or abarcode region, in for example, a shipping label.

In one embodiment, the segmentation process is used to identify thelocation of a signature in image data the might include additionalelements such as dataforms, text, graphics, images and the like. In onesuch embodiment the generalized classifier module 216 classifies thecontents of each region of the segmented image data. The regioncontaining the signature is then extracted by the signature dataprocessing module 218. In one embodiment if multiple regions areindicated as containing signature data, the signature data processingmodule 218 analyzes the arrangement of these regions to identify theregion most likely to contain the image data. In a further embodimentwhen multiple regions are indicated as containing signature data, theimage processing and analysis module establishes a feedback loop whereadditional segmented regions are generated and analyzed until a singlesegmented region containing signature data is located.

In an additional embodiment in which an image to be collected contains avaried combination of data types, the classification of the segmentedregions is used to initiate appropriate processing for each region. Forexample, for a region containing signature data, the signature iscaptured; for a region containing typed or machine readable text, an OCRanalysis is performed; for graphic elements, a graphics analysis isperformed; and the like.

In some embodiments, a predefined order or sequence of operationsinvolving the image reader 14 is known to occur. In these embodiments,the predefined order can be used to increase the efficiency of theoperation of the image reader 14 and the data collection processes. Anexample of such an embodiment is a situation where a signature captureoccurs subsequent to the reading and decoding of a certain type ofdataform. A process 400 of one such embodiment is shown in FIG. 9. Theprocess 400 includes the operator scanning (step 404) a particular typeof dataform. The image reader 14 then decodes and processes (step 408)the dataform and outputs (step 412) decoded dataform data. In responseto the processing of the particular type of dataform, the image readerenters (step 416) an image capture state. In this state when theoperator next actuates the trigger (step 420), the image reader 14collects (step 424) a frame of image data and either stores or transmits(step 428) the image data. One example of such an embodiment is aprocess that can be employed by the shipping agent described above. Inthis example, the shipping agent first scans the shipping label barcode34 a. In response to the decoding of this barcode 34 a, the image reader14 enters a signature capture mode. Next, the shipping agent, knowingthat the image reader 14 is prepared to capture a signature, aims theimage reader 14 at the signature 34 b and pulls the trigger 32. Theimage reader 14 then collects an image of the signature and eitherstores the image or transmits the image to the package tracking system42. In this embodiment, the capture of the signature can occur morequickly because the image reader 14 does not process the image data withthe dataform decode module 204 or the image processing and analysismodule 208.

An additional example of an environment in which the image reader 14 canbe employed is in pharmacy to facilitate the filling of a prescription.Frequently regulations now require that a person receiving aprescription must sign for the prescription. A copy of the signaturemust be collected and provided to regulators to ensure that the personcollecting the prescription is the person authorized to do so. Accordingto one embodiment used in this environment, an optional first step isthat the image reader 14 can be used to collect an image of theprescription provided by a hospital or a doctor. The prescription can behandwritten, typed, or encoded by a barcode. If the prescription istyped or encoded in a barcode, the image reader 14 can decode theinformation contained therein for record keeping and for presentation tothe pharmacist. Once the pharmacist has filled the prescription, a labelfor the prescription container is printed. Frequently the prescriptionlabel includes barcode encoding information regarding the prescriptionsuch as one or more of the date it was filled, the type of medicine, thepatient's name, the patient's medical condition, the patent's insuranceprovider or Medicare number, and the like. To prove receipt of themedicine by the proper party, the patient signs the label. As the labelis attached to the prescription, previously a photocopy or a carbon copyof the label with the signature was necessary for verification purposesfor the pharmacy. With the current invention, the information in thelabel can be captured in a quick and efficient manner with the imagereader 14. The pharmacist can scan the barcode on the label to create arecord and then scan the signature as proof of distribution inaccordance with regulations. As described above in one embodiment, thepharmacist takes one image of the label including the barcode and thesignature. The image reader 14 then automatically locates andappropriately processes the barcode, the signature, and any additionalrequired data.

Further examples of situations in which the image reader 14 can beemployed include commercial transactions in which a copy of a signedreceipt is required. For example if a customer has paid for a meal at arestaurant or purchased goods or services at a retail establishment orsupermarket, frequently he or she will be required to sign a receipt.The business establishment usually retains a copy of the signature asproof of payment for their records. Using the image reader 14, however,the employee receiving the signed receipt can easily and efficientlycapture the signature as an electronic record. This allows for theimmediate disposal of the receipt thereby simplifying record keeping andtransaction processing operations.

Referring again to FIG. 3, the following description provides additionaldetails on modules in the image reader 14 presented above. In variousembodiments, the processor module 104 can include PCBs, standard andproprietary electrical components, and generic and function specificintegrated circuits such as microprocessors and application specificintegrated circuit (ASIC). The memory module 108 can comprise any one ormore of read-only (ROM), random access (RAM) and non-volatileprogrammable memory for data storage. The ROM-based memory can be usedto accommodate security data and image reader 14 operating systeminstructions and code for other modules. The RAM-based memory can beused to facilitate temporary data storage during image reader operation.Non-volatile programmable memory may take various forms, erasableprogrammable ROM (EPROM) and electrically erasable programmable ROM(EEPROM) being typical. In some embodiments, non-volatile memory is usedto ensure that the data is retained when the image reader 14 is in itsquiescent or power-saving “sleep” state.

The image collection module 116 can be designed to collect optical,infrared, or ultraviolet electromagnetic signals, depending on thecomposition of lenses and materials of construction of the imagingsensor thereof. For example, a silicon sensor operating with commonplastic or glass lenses can detect signals in the visible region of theelectromagnetic spectrum. A gallium arsenide or other III-V compoundsemiconductor sensor operating with quartz optics can detect signals inthe ultraviolet. Infrared sensors, such as the IMD series ofpyroelectric infrared sensors available from Murata ManufacturingCompany, Ltd., of Tokyo, Japan, can be used to detect the presence ofinfrared signals.

In embodiments where the image collection module 116 is an electroniccamera or a CCD or CMOS array, the signals obtained from the opticalimaging device can be decoded with a suitably programmed computer ormicroprocessor, and can represent many different kinds of information,as described below in more detail. In one embodiment, the imagecollection module 116 is any one of the IMAGETEAM™ linear or area (2D)imaging engines, such as the 4000 OEM 2D Image Engine, available fromHand Held Products, Inc.

The I/O module 112 is used to establish potentially bi-directionalcommunications between the image reader 14 and other electronic devices.Examples of elements that can comprise a portion of the I/O module 112include a wireless or wired Ethernet interface, a dial-up or cable modeminterface, a USB interface, a PCMCIA interface, a RS232 interface, anIBM Tailgate Interface RS485 interface, a PS/2 keyboard/mouse port, aspecialized audio and/or video interface, a CompactFlash interface, a PCCard Standard interface, a Secure Digital standard for memory, a SecureDigital Input Output for input/output devices and/or any other standardor proprietary device interface. A CompactFlash interface is aninterface designed in accordance with the CompactFlash standard asdescribed in the CompactFlash Specification version 2.0 maintained atthe website http://www.compactflash.org. The CompactFlash Specificationversion 2.0 document is herein incorporated by reference in itsentirety. A PC Card Standard interface is an interface designed inaccordance with the PC Card Standard as described by, for example, thePC Card Standard 8.0 Release—April 2001 maintained by the PersonalComputer Memory Card International Association (PCMCIA) and availablethrough the website at http://www.pcmcia.org. The PC Card Standard 8.0Release—April 2001 Specification version 2.0 document is hereinincorporated by reference in its entirety.

The actuation module 124 is used to initiate the operation of variousaspects of the image reader 14 such as data collection and processing.In a hand-held embodiment of the image reader 14, the actuation modulecomprises a finger actuatable trigger 32. In a fixed mounted embodimentof the image reader 14, the actuation module 124 comprises an objectsensing module that initiates the operation of the image reader 14 whenthe presence of an object to be imaged is detected.

The user feedback module 128 is used to provide some form of sensoryfeedback to an operator. In various embodiments, the feedback caninclude an auditory signal such as a beep alert or tone, a visualdisplay such as a flashing indicator, a mechanical sensation such avibration in the image reader 14, or any other sensory feedback capableof indicating to an operator the status of operation of the image reader14 such as a successful image capture.

The display module 132 is used to provide display information such asoperational status such as battery or memory capacity, mode ofoperation, or other information to an operator. In various embodimentsthe display module 132 can be provided by a LCD flat panel display withan optional touch-pad screen overlay for receiving operator tactileinput coordinated with the display.

The user interface module 134 is used to provide an interface mechanismbetween an operator and the image reader. In various embodiments, theuser interface module 134 comprises a keypad, function specific orprogrammable buttons, a joy or toggle switch and the like. As indicatedabove, if the display module 132 includes a touch-pad screen overlay,the display module can incorporate some of the input functionalityalternatively provided by elements in the user interface module 134.

In some embodiments, the RFID module 136 is an ISO/IEC 14443 compliantRFID interrogator and reader that can interrogate a RFID contactlessdevice and that can recover the response that a RFID tag emits. TheInternational Organization for Standardization (ISO) and theInternational Electrotechnical Commission (IEC) are bodies that definethe specialized system for worldwide standardization. In otherembodiments, the RFID module 136 operates in accordance with ISO/IEC10536, or ISO/IEC 15963. Contactless Card Standards promulgated byISO/IEC cover a variety of types as embodied in ISO/IEC 10536 (Closecoupled cards), ISO/IEC 14443 (Proximity cards), and ISO/IEC 15693(Vicinity cards). These are intended for operation when very near,nearby and at a longer distance from associated coupling devices,respectively. In some embodiments, the RFID module 136 is configured toread tags that comprise information recorded in accordance with theElectronic Product Code (EPC), a code format proposed by the Auto-IDCenter at MIT. In some embodiments, the RFID module 136 operatesaccording to a proprietary protocol. In some embodiments, the RFIDmodule 136 communicates at least a portion of the information receivedfrom an interrogated RFID tag to a computer processor that uses theinformation to access or retrieve data stored on a server accessible viathe Internet. In some embodiments, the information is a serial number ofthe RFID tag or of the object associated with the RFID tag.

In some embodiments, the smart card module 140 is a ISO/IEC 7816compliant smart card reader with electrical contact for establishingcommunication with a suitably designed contact chip based smart card.The smart card module 140 is able to read and in some cases write datato attached smart cards.

In some embodiments, the magnetic stripe card module 144 is a magneticstripe reader capable of reading objects such as cards carryinginformation encoded in magnetic format on one or more tracks, forexample, the tracks used on credit cards. In other embodiments, themagnetic stripe card module 144 is a magnetic character reading device,for reading characters printed using magnetic ink, such as is found onbank checks to indicate an American Bankers Association routing number,an account number, a check sequence number, and a draft amount. In someembodiments, both types of magnetic reading devices are provided.

In some embodiments of the image reader 14, the functionality of theRFID module 136, the smart card module 140, and the magnetic stripe cardmodule 144 are combined in a single tribrid reader module such as thePanasonic's Integrated Smart Card Reader model number ZU-9A36CF4available from the Matsushita Electrical Industrial Company, Ltd.

One example of an image reader 14 constructed in accordance with theinvention is shown in the perspective drawing of the hand-held imagereader 14 a of FIG. 10. The hand-held image reader 14 a includes animager 252 having a lens 254 and a plurality of light sources 258, adisplay 262, interface indicators 266, interface buttons 270, and atrigger 32. In the embodiment shown, the hand-held image reader 14 aalso includes a cable 274 attached to a plug 278 including a pluralityof pins 282 for establishing electrical communication with a computingdevice. In various embodiments, the hand-held image reader 14 a can beany of the IMAGETEAM™ linear or area image readers such as the models3800, 3870 and 4410 available from Hand Held Products, Inc. constructedin accordance with the invention.

Another example of an image reader 14 constructed in accordance with theprinciples of the invention is the portable data terminal 14 b shown indifferent perspective drawings in FIGS. 11A-11D. FIG. 11A shows a topperspective, FIG. 11B shows a bottom perspective, FIG. 11C shows a frontperspective view, and FIG. 11D shows a back perspective view. As shown,the portable data terminal 14 b in one embodiment includes interfaceelements including a display 504, a keyboard 508, interface buttons 512for example for positioning a cursor, an actuator 516, and a stylus 520with a stylus holder 524. The portable data terminal 14 b furtherincludes an imager 528. In additional embodiments the portable dataterminal can have its functionality enhanced with the addition ofmultiple detachable computer peripherals. In various embodiments, thecomputer peripherals can include one or more of a magnetic stripereader, a biometric reader such as a finger print scanner, a printersuch as a receipt printer, a RFID tag or RF payment reader, a smart cardreader, and the like.

The portable data terminal 14 b further includes an electro-mechanicalinterface 532 such as a dial-up or cable modem interface, a USBinterface, a PCMCIA interface, an Ethernet interface, a RS232 interface,an IBM Tailgate Interface RS485 interface, a CompactFlash interface, aPC Card Standard interface, a Secure Digital standard for memoryinterface, a Secure Digital Input Output for input/output devicesinterface and/or any other appropriate standard or proprietary deviceinterface. In various embodiments the electro-mechanical interface 532can be used as part of attaching computer peripherals.

An electrical block diagram of one embodiment of the portable dataterminal 14 b is shown in FIG. 12. In the embodiment of FIG. 12, theimager 528 includes an image engine including two-dimensional imagesensor 536 provided on image sensor chip 546 and associated imagingoptics 544. Image sensor chip 546 may be provided in an IT4000 or IT4200image engine of the type available from HHP, Inc. of Skaneateles Falls,N.Y. The portable data terminal 14 b further includes a processorintegrated circuit (IC) chip 548 such as may be provided by, forexample, an INTEL Strong ARM RISC processor or an INTEL PXA255Processor. Processor IC chip 548 includes a central processing unit(CPU) 552. As indicated above, the portable data terminal 14 b mayinclude a display 504, such as a liquid crystal display, a keyboard 508,a plurality of communication or radio transceivers such as a 802.11radio communication link 556, a Global System for MobileCommunications/General Packet Radio Service (GSM/GPRS) radiocommunication link 560, and/or a blue tooth radio communication link564. In additional embodiments, the portable data terminal 14 b may alsohave the capacity to transmit information such as voice or datacommunications via Code Division Multiple Access (CDMA), CellularDigital Packet Data (CDPD), Mobitex cellular phone and data networks andnetwork components. In other embodiments, the portable data terminal 14b can transmit information using a DataTAC™ network or a wirelessdial-up connection.

The portable data terminal 14 b may further include an infrared (IR)communication link 568. The keyboard 508 may communicate with IC chip548 via microcontroller chip 572. The portable data terminal 14 bfurther includes a memory 574 including a volatile memory and anon-volatile memory. The volatile memory in one embodiment is providedin part by a RAM 576. The non-volatile memory may be provided in part byflash ROM 580. Processor IC chip 548 is in communication with the RAM576 and ROM 580 via a system bus 584. Processor IC chip 548 andmicrocontroller chip 572 also include areas of volatile and non-volatilememory. In various embodiments where at least some of the modulesdiscussed above, such as the autodiscrimination module 120, areimplemented at least in part in software, the software components can bestored in the non-volatile memories such as the ROM 580. In oneembodiment, the processor IC chip 548 includes a control circuit thatitself employs the CPU 552 and memory 574. Non-volatile areas of thememory 574 can be used, for example, to store program operatinginstructions.

In various embodiments, the processor IC chip 548 may include a numberof I/O interfaces (not all shown in FIG. 12) including several serialinterfaces (e.g., general purpose, Ethernet, blue tooth), and parallelinterfaces (e.g., PCMCIA, Compact Flash).

For capturing images, the processor IC chip 548 sends appropriatecontrol and timing signals to image sensor chip 546. The processor ICchip 548 further manages the transfer of image data generated by thechip 546 into the RAM 576. In one embodiment, the processor IC chip 548processes frames of image data to, for example, decode a bar code or aset of OCR characters, to perform a texture analysis, and/or to performa classification of the processing data generated by, for example, thegeneralized classifier module 216. Various bar code and OCR decodingalgorithms are commercially available, such as by the incorporation ofan IT4250 image engine with decoder board, available from HHP, Inc. Inone embodiment, the decoder board decodes symbologies such as MaxiCode,PDF417, MicroPDF417, Aztec, Aztec Mesa, Data Matrix, QR Code, Code 49,UCC Composite, Snowflake, Vericode, Dataglyphs, Code 128, Codabar,UPC/EAN, Interleaved 2 of 5, RSS, BC 412, Code 93, Codablock, Postnet(US), BPO4 State, Canadian 4 State, Japanese Post, KIX (Dutch Post),Planet Code, OCR A, OCR B, and the like.

Among other operations, the infrared transceiver 568 facilitatesinfrared copying of data from a portable data terminal 14 b in abroadcasting mode to a portable data terminal 14 b in a receiving mode.Utilization of infrared transceiver 568 during a data copying sessionallows data broadcast from a single broadcast device to besimultaneously received by several receiving devices without any of thereceiving devices being physically connected to the broadcasting device.In an additional further embodiment, the image reader 14 can becontained in a transaction terminal.

Referring to FIGS. 13A-13C and 14 an additional system and method forcapturing signatures are presented. The system and method are based onthe use of a progressive random pattern, alternatively called aprogressive diffused pattern, to locate a signature image area. FIG. 13Aprovides an example of one embodiment of a progressive random pattern.As shown the progressive random pattern is characterized by a randomelement pattern that shifts from dense to sparse. The elements in thepattern can be of any shape so long as they maintain a predeterminedminimum resolution such as 15 or 20 mils. For any random scan across theprogressive random pattern, the progressive random pattern ismathematically defined by the following. A first predefined number N1 oftransitions establishes a window width W. Along a scan line, such as thehorizontal line 592 shown in FIG. 13A, a transition is encountered whenthe binarized pixel values change from black (i.e., pixel value 0) towhite (i.e., pixel value 1) or vice versa. The width W is the distancefrom the first to the last of the N1 transitions. In one embodiment, thefollowing relationship for the number of transitions N2 encountered inthe second width W must be satisfied:N1=R1(N2)=R1*N2+/−10%This relationship states that the number of transitions N2 located inthe second width W of the scan line multiplied by the factor R2 mustequal the first number of transitions N1 to within a margin of error ofplus or minus ten percent. For a longer and more precisely definedprogressive random pattern, the number of transitions in each successivewidth W is defined with relationship to the preceding number and anadditional numeric factor. For example for the third width W in the scanline, the number N3 of transitions encountered multiplied by the factorR2 is equal to the number of transitions N2 to within a margin of plusor minus ten percent. This is summarized by:N2=R2(N3)=R2*N3+/−10%

In one embodiment, the numeric factors R1, R2, R3, etc. are defined asintegers. Therefore, as long as R1 does not equal R2, the direction ofthe scan line can be determined. This in turn allows the image reader 14to determine whether the signature is above or below the progressiverandom pattern (i.e., whether the image data is inverted or not).

Referring to FIGS. 13B and 13C two embodiments for placing a signaturein relation to the progressive random patterns are shown. In FIG. 13Bthe signature is displaced slightly vertically above the progressiverandom pattern. In FIG. 13C the signature is written on top of theprogressive random pattern. FIG. 13C demonstrates that the progressiverandom pattern scheme for capturing a signature is robust to defects,distortions or additions introduced into the progressive random pattern.Placing the signature on top of the progressive random pattern has theconsumer privacy advantage that the signed material can not bephotocopied without detection. In addition, the progressive randompattern approach has the advantage that the pattern is generally moreascetically pleasing to a human user than placing a barcode in proximityto the signature. In addition as the signature can be placed immediatelyadjacent to the or even on top of the progressive random pattern, theprogressive random pattern scheme allows a human operator of an imagerreader to intuitively aim the image reader 14 at the signature tocapture the signature. As the identification of a progressive randompattern is not necessarily adversely affected by the presence of straysignature marks, a progressive random pattern in some embodiments can beplaced closer to a signature line than could a barcode. With manybarcodes, stray marks adversely affect the ability of an image reader toproperly read and decode the barcode, and, therefore, a barcode used toidentify the location of a signature must typically be placed far enoughfrom the signature line so that there is not a significant risk of straysignature marks tainting the barcode.

FIG. 14 presents a process 600 for capturing a signature with aprogressive random pattern scheme. To initiate signature capture, ahuman operator aims (step 604) an image reader 14 at the progressiverandom pattern and collects (step 608) image data including at least theprogressive random pattern and the signature. The image reader 14extracts (step 612) a one-dimensional slice of the image data. The imagereader 14 then analyzes (step 616) the image data slice for the presenceof a progressive random pattern. Candidates for the start of theprogressive random pattern are identified as regions of transition datasubsequent to a region of blank data of a specified length, such as aquiet zone. The analysis includes determining whether the scan line datamatches the mathematical criteria established for a progressive randompattern discussed above. As indicated above this can include verifyingthe relationship between N1 and N2 and possible N2 and N3 and furthertransition numbers. If the results of the analysis for a single scanline of data are inconclusive, one or more additional scan lines can beextracted and analyzed (step 620). Once the existence of a progressiverandom pattern is confirmed, the location of the start of theprogressive random pattern is determined (step 624) by locating thestart of the first window containing the N1 transitions. The signaturedata area is then established (step 628) by applying vertical andhorizontal offsets to the first window start location. In one embodimentif the image data already collected includes the signature data area,then the signature data area is extracted (step 632) from the imagedata, possibly compressed (step 634) and stored (step 636) in memory. Inanother embodiment, if the signature data area is not included in theoriginal image or if the original image was of a low resolution, anaddition image is collected that includes the signature data area and/orthat is of a higher resolution.

In various embodiments, the modules discussed above including theprocessor module 104, the memory module 108, the I/O module 112, theimage collection module 116, autodiscrimination module 120, theactuation module 124, the user feedback module 128 the user interfacemodule 134, the RFID module 136, the smart card module 140, the magneticstripe module 144, the dataform decode module 204 the image processingand analysis module 208 the feature extraction module 212, thegeneralized classifier module 216, the signature data processing module218, the OCR decode module 222, the graphics analysis module, thebinarizer module 226, the line thinning module 228, and the convolutionmodule 230 can be implemented in different combination of software,firmware, and/or hardware.

As used herein the term image reader is used to generally cover imagecollection devices that can perform various functions such as imagecollection, image analysis, image processing, and/or image datadecoding.

Machine-readable storage media that can be used in the invention includeelectronic, magnetic and/or optical storage media, such as magneticfloppy disks and hard disks; a DVD drive, a CD drive that in someembodiments can employ DVD disks, any of CD-ROM disks (i.e., read-onlyoptical storage disks), CD-R disks (i.e., write-once, read-many opticalstorage disks), and CD-RW disks (i.e., rewriteable optical storagedisks); and electronic storage media, such as RAM, ROM, EPROM, CompactFlash cards, PCMCIA cards, or alternatively SD or SDIO memory; and theelectronic components (e.g., floppy disk drive, DVD drive, CD/CD-R/CD-RWdrive, or Compact Flash/PCMCIA/SD adapter) that accommodate and readfrom and/or write to the storage media. As is known to those of skill inthe machine-readable storage media arts, new media and formats for datastorage are continually being devised, and any convenient, commerciallyavailable storage medium and corresponding read/write device that maybecome available in the future is likely to be appropriate for use,especially if it provides any of a greater storage capacity, a higheraccess speed, a smaller size, and a lower cost per bit of storedinformation. Well known older machine-readable media are also availablefor use under certain conditions, such as punched paper tape or cards,magnetic recording on tape or wire, optical or magnetic reading ofprinted characters (e.g., OCR and magnetically encoded symbols) andmachine-readable symbols such as one and two dimensional bar codes.

Those of ordinary skill will recognize that many functions of electricaland electronic apparatus can be implemented in hardware (for example,hard-wired logic), in software (for example, logic encoded in a programoperating on a general purpose processor), and in firmware (for example,logic encoded in a non-volatile memory that is invoked for operation ona processor as required). The present invention contemplates thesubstitution of one implementation of hardware, firmware and softwarefor another implementation of the equivalent functionality using adifferent one of hardware, firmware and software. To the extent that animplementation can be represented mathematically by a transfer function,that is, a specified response is generated at an output terminal for aspecific excitation applied to an input terminal of a “black box”exhibiting the transfer function, any implementation of the transferfunction, including any combination of hardware, firmware and softwareimplementations of portions or segments of the transfer function, iscontemplated herein. This application incorporates by reference U.S.Patent Publication No. 2006/0071081.

In one aspect the invention features a method for automaticallydiscriminating between a dataform and a signature. The method comprisescollecting image data from an object with an image reader; analyzing theimage data to automatically discriminate between a dataform and asignature; and outputting the processed image data. In variousembodiments of the method, outputting the processed image data comprisesproducing decoded barcode data and/or capturing a signature. In oneembodiment of the method, analyzing the image data to automaticallydiscriminate between a barcode and a signature comprises attempting todecode the image data as dataform data; and testing the image data forcharacteristics indicative of a signature, if the attempt to decode theimage data as dataform data fails. In an additional embodiment,analyzing the image data and outputting processed image data compriseattempting to decode the image data as dataform data, testing the imagedata for characteristics indicative of a signature, outputting decodeddataform data if the attempt to decode the image data as dataform datais successful, and outputting signature data if the test forcharacteristics indicative of a signature is successful. In anotherembodiment of the method, the attempt to decode the image data as barcode data comprises an attempt to decode the image data as a onedimensional bar code. In a further such embodiment, the one dimensionalbar code is a UPC bar code. In an additional embodiment of the method,testing the image data for characteristics indicative of a signatureincludes at least testing the image data for the presence of curvedelements.

In another aspect, the invention features a method for automaticallydiscriminating between a barcode and a signature. The method comprisesaiming a hand-held image reader at an object including a target;actuating a trigger on the hand-held image reader; collecting image dataincluding at least the target; analyzing the image data to automaticallydiscriminate between a barcode and a signature; and outputting processedimage data. In various embodiments of the method, outputting processedimage data comprises producing decoded barcode data and/or capturing asignature. In one embodiment of the method, analyzing the image data toautomatically discriminate between a barcode and a signature comprisesattempting to decode the image data as dataform data and testing theimage data for characteristics indicative of a signature, if the attemptto decode the image data as dataform data fails. In an additionalembodiment analyzing the image data and outputting processed image datacomprise attempting to decode the image data as dataform data, testingthe image data for characteristics indicative of a signature, outputtingdecoded dataform data if the attempt to decode the image data asdataform data is successful, and outputting signature data if the testfor characteristics indicative of a signature is successful. In anotherembodiment of the method, the attempt to decode the image data as barcode data comprises an attempt to decode the image data as a onedimensional bar code. In a further such embodiment, the one dimensionalbar code is a UPC bar code. In an additional embodiment of the method,testing the image data for characteristics indicative of a signatureincludes at least testing the image data for the presence of curvedelements.

In another aspect, the invention features an image reader thatautomatically discriminates between a barcode and a signature. The imagereader comprises an image collection module configured to collect imagedata representative of a target and an autodiscrimination module incommunication with the image collection module. The autodiscriminationmodule is configured to automatically discriminate between a signatureand a dataform. The image reader also comprises an I/O module incommunication with at least the autodiscrimination module. In oneembodiment, the autodiscrimination module comprises a dataform decodermodule and an image processing and analysis module that is configured toperform a texture analysis of the image data and that is configured toclassify the image data in response to the texture analysis. In anadditional embodiment, the texture analysis includes testing the imagedata for characteristics indicative of a signature. In a furtherembodiment, testing the image data for characteristics indicative of asignature includes at least testing the image data for the presence ofcurved elements. In yet another embodiment, the image processing andanalysis module is configured to respond to a failure of the dataformdecoder to decode the image data. In yet an additional embodiment, thedataform decoder module is configured to execute a one dimensionalbarcode decoding algorithm. In a related embodiment, the one dimensionalbarcode decoding algorithm is configured to decode at least UPCbarcodes. In yet a further embodiment, the image reader also comprises ahousing configured to be hand held and oriented by a human operator anda trigger configured to be actuated to initiate the operation of theautodiscrimination module and the image collection module. The housingencloses the autodiscrimination module, the image collection module, andthe output module. In still another embodiment, the image reader furthercomprises a housing to be attached to a surface. The housing enclosesthe autodiscrimination module, the image collection module, and theoutput module. When attached to the surface, the housing is designed toorient the image collection module towards objects containing targets tobe imaged by the image collection module. In various embodiments, theimage collection module and the output module are implemented in acombination of hardware and software.

In another aspect, the invention features a method for automaticallycapturing a signature on a signature bearing item. The method comprisescollecting image data from an object with an image collecting device,the image data representing a signature or a barcode. The method alsocomprises analyzing the image data to determine whether the image datarepresents a signature and capturing the signature contained in thesignature image data if the analysis of the image data indicates thatthe signature is present in the image data.

In another aspect the invention features a method for automaticallycapturing a signature on a signature bearing item. The method comprisescapturing a first image with an image reader, the first image includinga representation of a dataform; decoding and processing the dataform;and capturing a second image, the second image including arepresentation of a signature. The capturing of the second image isperformed in response to at least the decoding and processing of thedataform. In one embodiment, capturing the first image occurs inresponse to a first actuation of a trigger on the image reader andcapturing the second image occurs in response to at least a secondactuation of the trigger.

In another aspect the invention features, an image reader that capturesan image of a dataform and a signature. The image reader comprises animage collection module configured to collect image data representativeof a target, the image data including representations of a dataform anda signature; a dataform decode module in communication with the imagecollection module, the dataform decode module configured to decode thedataform in the image data; and a signature data processing module, thesignature data processing module in communication with the imagecollection module. The signature data processing module configured tostore a copy of the image data including the dataform and the signature.

In another aspect, the invention features an image reader that capturesan image of a dataform and a signature. The image reader comprises animage collection module configured to collect image data representativeof a target, the image data including representations of a dataform anda signature; a dataform decode module in communication with the imagecollection module, the dataform decode module configured to decode thedataform in the image data; and an image processing and analysis modulein communication with the image collection module. The image processingand analysis module configured to determine the location of thesignature in the image data by at least analyzing the image data forcharacteristics indicative of a signature. In one embodiment, thedetermination of the location of the signature in the image dataincludes analyzing a plurality of regions of the image data forcharacteristics indicative of a signature. In another embodiment, theimage processing and analysis module comprises an image modificationmodule that generates modified image data in which a portion of theimage data not including the signature has been deleted.

In another aspect, the invention features a method for automaticallydiscriminating between a plurality of data types. The method comprisescollecting image data from an object with an image collecting device;analyzing the image data to automatically discriminate between at leasta first data type and a second data type, the second data type beingdifferent from the first data type; and processing the image data. Inthis embodiment, the first data type and the second data type areselected from the group consisting of: signatures; dataforms;handwritten text; typed text; machine readable text; OCR data; pictures;images; shipping manifests; finger prints; and biometrics. In yetanother embodiment, the biometric data comprises a facial image scan. Inyet an additional embodiment, processing the image data comprisesproducing decoded barcode data. In yet a further embodiment, processingthe image data comprises capturing a signature. In still anotherembodiment, analyzing the image data to automatically discriminatebetween at least a first data type and a second data type comprisesattempting to decode the image data as dataform data and testing theimage data for characteristics indicative of a signature, if the attemptto decode the image data as dataform data fails. In still a furtherembodiment, the attempt to decode the image data as bar code datacomprises an attempt to decode the image data as a one dimensional barcode. In an additional such embodiment, the one dimensional bar code isa UPC bar code. In another embodiment, testing the image data forcharacteristics indicative of a signature includes at least testing theimage data for the presence of curved elements.

In another aspect, the invention features a method for automaticallyidentifying a signature. The method comprises collecting image data froman object with an image collecting device; analyzing the image data toautomatically determine whether a signature is contained in the imagedata; outputting a first response if the signature is present in theimage data; and outputting a second response if the signature is absentfrom the image data.

While the present invention has been explained with reference to thestructure disclosed herein, it is not confined to the details set forthand this invention is intended to cover any modifications and changes asmay come within the scope and spirit of the following claims.

I claim:
 1. An image reader that automatically discriminates betweendata types, the image reader comprising: an image collection moduleconfigured to collect image data representative of a target; anautodiscrimination module in communication with the image collectionmodule, the autodiscrimination module configured to automaticallydiscriminate between a facial image biometric and a dataform; and an I/Omodule in communication with at least the autodiscrimination module. 2.The image reader according to claim 1, wherein said autodiscriminationmodule comprises: a dataform decoder module; and an image processing andanalysis module configured to perform a texture analysis of the imagedata and configured to classify the image data in response to thetexture analysis.
 3. The image reader according to claim 2, wherein theautodiscrimination module is configured to automatically discriminatebetween a signature and a dataform, and wherein the texture analysisincludes testing the image data for characteristics indicative of asignature.
 4. The image reader according to claim 3, wherein testing theimage data for characteristics indicative of a signature includes atleast testing the image data for the presence of curved elements.
 5. Theimage reader according to claim 1, further comprising: a housingconfigured to be hand held and oriented by a human operator, the housingenclosing the autodiscrimination module, the image collection module,and the I/O module; and a trigger configured to be actuated to initiatethe operation of the autodiscrimination module and the image collectionmodule.
 6. The image reader according to claim 1, wherein theautodiscrimination module is configured to automatically discriminatebetween a signature and a dataform.
 7. The image reader according toclaim 1, wherein the autodiscrimination module includes a generalizedclassifier module that employs a neural network and receives data fromthe feature extraction module.
 8. The image reader according to claim 1,wherein the image data is image data of a first collected image, whereinthe image reader is configured for identifying a bar code dataform inthe first collected image and for examining image data representing asignature to identify a signature in the first collected image, thefirst collected image collected using the image collection module, theimage reader further configured to attempt to decode the barcodedataform by processing image data of the first collected image and tooutput decoded barcode dataform data determined from the attempt todecode.
 9. The image reader according to claim 1, wherein the image datais image data of a first collected image, wherein the image readersegments the first collected image into a plurality of regions, whereinthe image reader for each region of the plurality of regions isconfigured to classify content of the region as a dataform if the regionincludes a dataform, and as a signature if the region includes asignature.
 10. The image reader according to claim 1, wherein the imagedata is image data of a first collected image, wherein the image readersubjects image data of a first collected image to a decode attempt usinga dataform decode module, the first collected image collected using theimage collection module, wherein the image reader is configured so thatresponsive to decoding of the dataform by subjecting of the image dataof a first collected image to a decode attempt the dataform decodemodule is disabled in a manner that for a subsequent image collectedusing the image collection module, the dataform decode module is notactive to process image data for attempting to decode a dataform.
 11. Animage reader that automatically identifies different data types, theimage reader comprising: an image collection module configured tocollect image data representative of a target; wherein the image readeris configured for identifying a bar code dataform in a first collectedimage and for examining image data representing a signature to identifya signature in the first collected image, the first collected imagecollected using the image collection module, the image reader furtherconfigured to attempt to decode the barcode dataform by processing imagedata of the first collected image and to output decoded barcode dataformdata determined from the attempt to decode.
 12. The image reader ofclaim 11, wherein the image reader is hand held.
 13. The image reader ofclaim 11, wherein the image reader is configured for storing into amemory signature image data responsive to the image reader identifyingthe signature.
 14. The image reader of claim 13, wherein for performingof the storing the image reader stores the first collected image withoutcropping of image data external to the signature image data.
 15. Theimage reader of claim 13, wherein for performing of the storing theimage reader compresses the first collected image.
 16. The image readerof claim 11, wherein the image reader is configured for generating amodified image by cropping image data external to the signatureresponsive to the image reader identifying the signature.
 17. The imagereader of claim 11, wherein the examining image data includes applyingcurvelet detector maps.
 18. The image reader of claim 11, wherein theimage reader segments the first collected image into a plurality ofregions, wherein the image reader for each region of the plurality ofregions is configured to classify content of the region as a dataform ifthe region includes a dataform, and as a signature if the regionincludes a signature.
 19. A image reader that automatically identifiesdifferent data types, the image reader comprising: an image collectionmodule configured to collect image data representative of a target;wherein the image reader segments a first collected image into aplurality of regions, wherein the image reader for each region of theplurality of regions is configured to classify content of the region asa dataform if the region includes a dataform, and as a signature if theregion includes a signature, the first collected image collected usingthe image collection module.
 20. The image reader of claim 19, whereinthe image reader is hand held.
 21. A image reader comprising: an imagecollection module configured to collect image data representative of atarget; a dataform decode module; wherein the image reader subjectsimage data of a first collected image to a decode attempt using thedataform decode module, the first collected image collected using theimage collection module, wherein the image reader is configured so thatresponsive to decoding of the dataform by subjecting of the image dataof a first collected image to a decode attempt the dataform decodemodule is disabled in a manner that for a subsequent image collectedusing the image collection module, the dataform decode module is notactive to process image data for attempting to decode a dataform. 22.The image reader of claim 21, wherein the image reader includes aclassifier that classifies image data of a collected image by data type,wherein further in response to the decoding of the dataform theclassifier is disabled in a manner that for the subsequent image, theclassifier is not active to classify image data by data type.